Making History Count introduces the main quantitative methods used in historical research. The emphasis is on intuitive understanding and application of the concepts, rather than formal statistics; no knowledge of mathematics beyond simple arithmetic is required. The techniques are illustrated by applications in social, political, demographic and economic history. Students will learn to read and evaluate the application of the quantitative methods used in many books and articles, and to assess the historical conclusions drawn from them. They will also see how quantitative techniques can open up new aspects of an enquiry, and supplement and strengthen other methods of research. This textbook will encourage students to recognize the benefits of using quantitative methods in their own research projects. The text is clearly illustrated with tables, graphs and diagrams, leading the student through key topics. Additional support includes five specific historical data-sets, available from the Cambridge website.
Making History Count introduces the main quantitative methods used in historical research. The emphasis is on intuitive understanding and application of the concepts, rather than formal statistics; no knowledge of mathematics beyond simple arithmetic is required. The techniques are illustrated by applications in social, political, demographic and economic history. Students will learn to read and evaluate the application of the quantitative methods used in many books and articles, and to assess the historical conclusions drawn from them. They will also see how quantitative techniques can open up new aspects of an enquiry, and supplement and strengthen other methods of research. This textbook will encourage students to recognize the benefits of using quantitative methods in their own research projects. The text is clearly illustrated with tables, graphs and diagrams, leading the student through key topics. Additional support includes five specific historical data-sets, available from the Cambridge website.
Inhaltsverzeichnis
Part I. Elementary Statistical Analysis: 1. Introduction; 2. Descriptive statistics; 3. Correlation; 4. Simple linear regression; Part II. Samples and Inductive Statistics: 5. Standard errors and confidence intervals; 6. Hypothesis testing; 7. Non-parametric tests; Part III. Multiple Linear Regression: 8. Multiple relationships; 9. The classical linear regression model; 10. Dummy variables and lagged values; Part IV. Further Topics in Regression Analysis: 11. Violating the assumptions of the classical model; 12. Non-linear models and functional forms; 13. Logit, probit, and tobit models; Part V. Specifying and Interpreting Models: Four Case Studies: 14. Case studies 1 and 2: unemployment in Britain and emigration from Ireland; 15. Case studies 3 and 4: the Old Poor Law in England and leaving home in the United States, 1850-60; Appendix A. The four data sets; Appendix B. Index numbers; Index.
